摘要
研究基于正态隶属函数的模糊神经网络的学习算法 .将模糊神经网络对一组样本的逼近误差表示为两组相互独立 ,可分批学习的可调参数的非负函数之和 .其中一组可调参数可通过令相应的非负函数为零直接求得 ,而与另一组可调参数相对应的非负函数就是用于这组参数学习的性能指标 .经对性能指标性质的分析给出了一种模糊神经网络的学习算法——二阶段变半径随机搜索法 .实例表明 ,这种方法简便易行 。
Taking normal functions as membership functions of fuzzy variables the approaching error of a fuzzy neural network to a group of samples is denoted as the sum of two nonnegative functions of two independent and adjustable groups of parameters that can be trained one after another. One of the two parameter groups can be obtained directly by taking its corresponding nonnegative functions to be zero and another parameter group can be obtained through learning according to its corresponding nonnegative functions performance index. Based on the analysis of the performance index a new algorithm, two stage random search algorithm with variable radius, is put forward. Some examples show that the algorithm is simple and convenient and can make fuzzy neural network attain high precision.
出处
《自动化学报》
EI
CSCD
北大核心
2000年第5期616-622,共7页
Acta Automatica Sinica
关键词
二阶段变半径随机搜索法
学习算法
模糊神经网络
Two-stage random search algorithm with variable radius, performance index, normal function, pseudoinverse.
